Bottom Line:
We found multiple previously unknown age- and gender-related metabolome changes of potential medical significance.Specifically, we found that the major developmental state differences between girls and boys are attributed to sphingolipids.We show that children have different development rates at the level of metabolome and thus the state-based approach may be advantageous when applying metabolome profiling in search of markers for subtle (patho)physiological changes.

Affiliation: Department of Information and Computer Science, Adaptive Informatics Research Centre and Helsinki Institute for Information Technology, Helsinki University of Technology, Espoo, Finland.

ABSTRACTLittle is known about the human intra-individual metabolic profile changes over an extended period of time. Here, we introduce a novel concept suggesting that children even at a very young age can be categorized in terms of metabolic state as they advance in development. The hidden Markov models were used as a method for discovering the underlying progression in the metabolic state. We applied the methodology to study metabolic trajectories in children between birth and 4 years of age, based on a series of samples selected from a large birth cohort study. We found multiple previously unknown age- and gender-related metabolome changes of potential medical significance. Specifically, we found that the major developmental state differences between girls and boys are attributed to sphingolipids. In addition, we demonstrated the feasibility of state-based alignment of personal metabolic trajectories. We show that children have different development rates at the level of metabolome and thus the state-based approach may be advantageous when applying metabolome profiling in search of markers for subtle (patho)physiological changes.

f1: Structure of the HMM used in this study. The model is made to focus on progressive changes over time by assuming that returning back in states is not possible after state 2. Separate HMM models are developed for both genders. The nodes in the graph represent the hidden states, each of which emits a multivariate profile of metabolite concentrations, and arrows represent possible transitions between the states.

Mentions:
We assumed that the observed trends in metabolic profiles were generated by a series of metabolic developmental states. HMMs (Rabiner, 1989) were applied to model the states. When designing the model structure, we assumed that the underlying states form a chain (Figure 1), thus constraining the model to focus on the progression of metabolite concentrations in time. To study differences between sexes, two separate models were trained, one for girls and one for boys.

f1: Structure of the HMM used in this study. The model is made to focus on progressive changes over time by assuming that returning back in states is not possible after state 2. Separate HMM models are developed for both genders. The nodes in the graph represent the hidden states, each of which emits a multivariate profile of metabolite concentrations, and arrows represent possible transitions between the states.

Mentions:
We assumed that the observed trends in metabolic profiles were generated by a series of metabolic developmental states. HMMs (Rabiner, 1989) were applied to model the states. When designing the model structure, we assumed that the underlying states form a chain (Figure 1), thus constraining the model to focus on the progression of metabolite concentrations in time. To study differences between sexes, two separate models were trained, one for girls and one for boys.

Bottom Line:
We found multiple previously unknown age- and gender-related metabolome changes of potential medical significance.Specifically, we found that the major developmental state differences between girls and boys are attributed to sphingolipids.We show that children have different development rates at the level of metabolome and thus the state-based approach may be advantageous when applying metabolome profiling in search of markers for subtle (patho)physiological changes.

Affiliation:
Department of Information and Computer Science, Adaptive Informatics Research Centre and Helsinki Institute for Information Technology, Helsinki University of Technology, Espoo, Finland.

ABSTRACTLittle is known about the human intra-individual metabolic profile changes over an extended period of time. Here, we introduce a novel concept suggesting that children even at a very young age can be categorized in terms of metabolic state as they advance in development. The hidden Markov models were used as a method for discovering the underlying progression in the metabolic state. We applied the methodology to study metabolic trajectories in children between birth and 4 years of age, based on a series of samples selected from a large birth cohort study. We found multiple previously unknown age- and gender-related metabolome changes of potential medical significance. Specifically, we found that the major developmental state differences between girls and boys are attributed to sphingolipids. In addition, we demonstrated the feasibility of state-based alignment of personal metabolic trajectories. We show that children have different development rates at the level of metabolome and thus the state-based approach may be advantageous when applying metabolome profiling in search of markers for subtle (patho)physiological changes.